Human-robot symbiosis framework on exoskeleton devices

In the near future, human-robot coexistence and symbiosis will be a common scenario in our society. Especially with the increasing number of patients with stroke or other neurological disorders and the gradually aging population, people may need wearable exoskeletons that actively assist human's movements. In designing these robots, physical humanrobot interaction (pHRI) plays an important role. How to let human and robot cooperatively perform motor tasks and help each other is a grand challenge. Our research establishes a human-robot physical symbiosis framework that biomimics human's behavior when performing interactive motor skills. The human and robot are modeled as two adaptive controllers in parallel with the plant (system under control). As a result, we will have two feedback controllers working together, constantly adapting to each other's behavior and optimally stabilizing the plant to achieve a common goal. In addition, we propose an inverse optimal control method to estimate human control strategy. This information can enable the robot to predict future consensus interactive behaviors in order to cooperate with the human effectively. Experimental verifications have been carried out using double inverted pendulum to simulate a human-robot cooperative balance task in MATLAB environment.

[1]  M. Kawato,et al.  Two is better than one: Physical interactions improve motor performance in humans , 2014, Scientific Reports.

[2]  Leon A. Petrosjan,et al.  Cooperative Differential Games , 2018 .

[3]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[4]  E. Burdet,et al.  A Framework to Describe, Analyze and Generate Interactive Motor Behaviors , 2012, PloS one.

[5]  Jongeun Choi,et al.  Solutions to the Inverse LQR Problem With Application to Biological Systems Analysis , 2015, IEEE Transactions on Control Systems Technology.

[6]  A.D. Kuo,et al.  An optimal control model for analyzing human postural balance , 1995, IEEE Transactions on Biomedical Engineering.

[7]  Stephen P. Boyd,et al.  Graph Implementations for Nonsmooth Convex Programs , 2008, Recent Advances in Learning and Control.

[8]  Duane T. McRuer,et al.  A Review of Quasi-Linear Pilot Models , 1967 .

[9]  R. A. R. C. Gopura,et al.  Control methodologies for upper limb exoskeleton robots , 2012, 2012 IEEE/SICE International Symposium on System Integration (SII).

[10]  R. Ivry,et al.  The coordination of movement: optimal feedback control and beyond , 2010, Trends in Cognitive Sciences.

[11]  Hugh Herr,et al.  Exoskeletons and orthoses: classification, design challenges and future directions , 2009, Journal of NeuroEngineering and Rehabilitation.

[12]  Sethu Vijayakumar,et al.  Adaptive Optimal Feedback Control with Learned Internal Dynamics Models , 2010, From Motor Learning to Interaction Learning in Robots.

[13]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2012 update: a report from the American Heart Association. , 2012, Circulation.

[14]  L. Mertz,et al.  The Next Generation of Exoskeletons: Lighter, Cheaper Devices Are in the Works , 2012, IEEE Pulse.

[15]  Tsu-Tian Lee,et al.  The inverse problem of linear optimal control for constant disturbance , 1986 .

[16]  Frank L. Lewis,et al.  Adaptive optimal control for continuous-time linear systems based on policy iteration , 2009, Autom..

[17]  Sheldon Baron,et al.  Adaptive Behavior in Manual Control and the Optimal Control Model , 1984 .